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ODMedit: uniform semantic annotation for data integration in medicine based on a public metadata repository

Overview of attention for article published in BMC Medical Research Methodology, June 2016
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

Mentioned by

twitter
1 tweeter
facebook
1 Facebook page
wikipedia
2 Wikipedia pages

Citations

dimensions_citation
26 Dimensions

Readers on

mendeley
61 Mendeley
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Title
ODMedit: uniform semantic annotation for data integration in medicine based on a public metadata repository
Published in
BMC Medical Research Methodology, June 2016
DOI 10.1186/s12874-016-0164-9
Pubmed ID
Authors

Martin Dugas, Alexandra Meidt, Philipp Neuhaus, Michael Storck, Julian Varghese

Abstract

The volume and complexity of patient data - especially in personalised medicine - is steadily increasing, both regarding clinical data and genomic profiles: Typically more than 1,000 items (e.g., laboratory values, vital signs, diagnostic tests etc.) are collected per patient in clinical trials. In oncology hundreds of mutations can potentially be detected for each patient by genomic profiling. Therefore data integration from multiple sources constitutes a key challenge for medical research and healthcare. Semantic annotation of data elements can facilitate to identify matching data elements in different sources and thereby supports data integration. Millions of different annotations are required due to the semantic richness of patient data. These annotations should be uniform, i.e., two matching data elements shall contain the same annotations. However, large terminologies like SNOMED CT or UMLS don't provide uniform coding. It is proposed to develop semantic annotations of medical data elements based on a large-scale public metadata repository. To achieve uniform codes, semantic annotations shall be re-used if a matching data element is available in the metadata repository. A web-based tool called ODMedit ( https://odmeditor.uni-muenster.de/ ) was developed to create data models with uniform semantic annotations. It contains ~800,000 terms with semantic annotations which were derived from ~5,800 models from the portal of medical data models (MDM). The tool was successfully applied to manually annotate 22 forms with 292 data items from CDISC and to update 1,495 data models of the MDM portal. Uniform manual semantic annotation of data models is feasible in principle, but requires a large-scale collaborative effort due to the semantic richness of patient data. A web-based tool for these annotations is available, which is linked to a public metadata repository.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 2%
Unknown 60 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 21%
Student > Ph. D. Student 8 13%
Student > Master 8 13%
Professor > Associate Professor 6 10%
Student > Bachelor 5 8%
Other 12 20%
Unknown 9 15%
Readers by discipline Count As %
Computer Science 21 34%
Medicine and Dentistry 13 21%
Social Sciences 3 5%
Agricultural and Biological Sciences 3 5%
Nursing and Health Professions 2 3%
Other 8 13%
Unknown 11 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 June 2016.
All research outputs
#5,766,490
of 21,124,412 outputs
Outputs from BMC Medical Research Methodology
#898
of 1,883 outputs
Outputs of similar age
#82,944
of 281,543 outputs
Outputs of similar age from BMC Medical Research Methodology
#3
of 5 outputs
Altmetric has tracked 21,124,412 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 1,883 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.4. This one has gotten more attention than average, scoring higher than 51% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 281,543 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.